class_docstr: A data iterator object to contain a static graph with a dynamically
  changing constant time difference temporal feature set (multiple signals).
  The node labels (target) are also temporal. The iterator returns a single
  constant time difference temporal snapshot for a time period (e.g. day or week).
  This single temporal snapshot is a Pytorch Geometric Data object. Between two
  temporal snapshots the features and optionally passed attributes might change.
  However, the underlying graph is the same.
  
  Args:
      edge_index (Numpy array): Index tensor of edges.
      edge_weight (Numpy array): Edge weight tensor.
      features (Sequence of Numpy arrays): Sequence of node feature tensors.
      targets (Sequence of Numpy arrays): Sequence of node label (target) tensors.
      **kwargs (optional Sequence of Numpy arrays): Sequence of additional attributes.
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code: def __next__(self):
      if self.t < len(self.features):
          snapshot = self[self.t]
          self.t = self.t + 1
          return snapshot
      else:
          self.t = 0
          raise StopIteration
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/signal/static_graph_temporal_signal.py
imports: import torch
  import numpy
  from typing import Sequence, Union
  from torch_geometric.data import Data
label_desc: Use image node_id 11 for calling the StaticGraphTemporalSignal obj's underlying member method code with example usage: obj.__next__() and returns: snapshot